1 Introduction
1.1 Usage of this file
This file serves to be a supplementary document that describes all the statistics results performed for this project. It may help to test some new questions that are not included in the corresponding slides.
1.2 Experiment designs
This file displays the results of the FaceWord project (data collected at NYU). There are two experiments in this project. In Experiment 1, Chinese participants viewed Chinese faces and characters in four conditions (Layout: intact, exchange [top and bottom parts were switched], top and bottom) and completed an additional localizer (Chinese faces, Chinese characters, objects, scrambled objects). In Experiment 2, English speakers viewed Chinese characters and English words in four conditions (Layout: intact, exchange, top [top parts of Chinese characters; left two letters for English words] and bottom [bottom parts of Chinese characters; right four letters for English words]) and completed an additional localizer (Caucasian faces, English words, objects, scrambled objects).
1.3 Introduction to the analyses included in this file
For the main runs, analysis is conducted for each ROI separately (FFA1, FFA2, VWFA, LOC).
For each ROI, three analyses are performed:
- Univariate analysis (Repeated-measures ANOVA) is performed to compare the neural responses (beta values) of different conditions.
- E1: 2(Chinese faces vs. Chinese Characters) * 4 (intact, exchange, top vs. bottom);
- E2: 2(Chinese characters vs. English words) * 4 (intact, exchange, top vs. bottom).
- Multivariate pattern analysis (MVPA) with
libsvmis used to decode different condition pairs (see below) and one-tail one-sample t-tests is used to test if the pair of conditions can be decoded [whether the accuracy is significantly larger than the chancel level (0.5); one-tail one-sample t-tests]. Leave-one(-run)-out cross-validation is applied. No normalized or demean were used.- Pairs in E1:
- face_intact vs. word_intact;
- face_intact vs. face_exchange;
- face_top vs. face_bottom;
- word_intact vs. word_exchange;
- word_top vs. word_bottom.
- Pairs in E2:
- English_intact vs. Chinese_intact;
- English_intact vs. English_exchange;
- English_left vs. English_right;
- Chinese_intact vs. Chinese_exchange;
- Chinese_top vs. Chinese_bottom.
- Pairs in E1:
- Similarity of top+bottom to intact vs. exchange: The dependent variable is the probability of top+bottom was decoded as Exchange conditions. Two-tail one-sample t-tests is used to test if top+bottom is more similar to exchange relative to intact.
- If the pattern of top+bottom is more similar to that of exchange relative to intact, the probability (of being decoded as exchange) should be significantly larger than the chance level (0.5).
- If the pattern of top+bottom is more similar to that of intact relative to exchange, the probability (of being decoded as exchange) should be significantly smaller than the chance level (0.5).
1.4 How the labels are defined for each ROI?
- Identify the vertex whose beta value is larger than the surrounding vertices (i.e., the local maxima) for each ROI based on the reference coordinates in previous literature.
- Dilate the region centering at the local maxima and only keep 50% of the “peripheral” vertices whose response were larger. This step is iterated until the size of the ROI reaches the pre-defined size (100mm^2), during which the vertices are masked by a pre-defined label at the threshold of p < .05. In other words, the p-values for all vertices in the labels are smaller than .05 (uncorrected).
1.5 How is the probability of top+bottom being decoded as exchange calculated?
The probability was estimated for each particiapnt separately:
- The patterns of top and bottom are combined with three different weights (0.5/0.5, 0.25/0.75, 0.75/0.25).
- Supported Vector Machine (
libsvm) is trained with the patterns of intact vs. exchange (10 runs). - The trained model is used to predict the probability of the combined patterns being decoded as exchange [for each run separately].
- The probability of top+bottom being decoded as exchange for each participant is calculated by averaging the probability for each run.
2 Preparations
3 Experiment 1: Chinese faces and Chinese characters for Chinese participants
3.1 Load and clean data
3.1.1 Label (ROI) information
3.1.1.1 Size of labels
The above table displays the size (in mm2) of each label for each participant. (NA denotes that this label is not available for that particiapnt.)
3.1.1.2 Number of vertices for each label
The above table displays the number of vertices for each label and each participant. (NA denotes that this label is not available for that particiapnt.)
3.1.1.3 Number of participants for each ROI
3.1.1.4 Number of remaining participants
The above table dispalys the number of participants included in the following analyses for each ROI. (VWFA is only found on the left hemisphere.)
3.1.2 Data for univariate analyses
3.1.3 Data of decoding
3.1.4 Data for the Similarity of top + bottom
3.2 Label:FFA1
3.2.1 Univariate analyses
3.2.1.1 rm-ANOVA
3.2.1.1.1 Left FFA1
3.2.1.1.1.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 11 0.26 1.96 .03 .19
## 2 Layout 2.01, 22.10 0.04 7.62 ** .04 .003
## 3 FaceWord:Layout 2.45, 26.95 0.02 3.34 * .01 .04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG##
## Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
##
## Sum Sq num Df Error SS den Df F value Pr(>F)
## (Intercept) 117.528 1 11.9130 11 108.5209 4.906e-07 ***
## FaceWord 0.519 1 2.9126 11 1.9603 0.1890514
## Layout 0.641 3 0.9259 33 7.6159 0.0005292 ***
## FaceWord:Layout 0.204 3 0.6724 33 3.3442 0.0308176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
##
## Mauchly Tests for Sphericity
##
## Test statistic p-value
## Layout 0.46436 0.19095
## FaceWord:Layout 0.72437 0.68072
##
##
## Greenhouse-Geisser and Huynh-Feldt Corrections
## for Departure from Sphericity
##
## GG eps Pr(>F[GG])
## Layout 0.66969 0.003017 **
## FaceWord:Layout 0.81654 0.041871 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## HF eps Pr(>F[HF])
## Layout 0.8196729 0.001362821
## FaceWord:Layout 1.0679942 0.030817557
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words 0.147 0.105 11 1.400 0.1891
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.0644 0.0484 33 1.331 0.5501
## intact - top 0.2115 0.0484 33 4.373 0.0006
## intact - bottom 0.1574 0.0484 33 3.255 0.0133
## exchange - top 0.1471 0.0484 33 3.042 0.0226
## exchange - bottom 0.0930 0.0484 33 1.923 0.2381
## top - bottom -0.0541 0.0484 33 -1.118 0.6809
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.21643 0.1165 16.4 1.857 0.0814
## exchange . faces - words 0.06828 0.1165 16.4 0.586 0.5659
## top . faces - words 0.25903 0.1165 16.4 2.223 0.0406
## bottom . faces - words 0.04451 0.1165 16.4 0.382 0.7074
## . faces intact - exchange 0.13845 0.0635 64.4 2.179 0.0330
## . faces intact - top 0.19017 0.0635 64.4 2.993 0.0039
## . faces intact - bottom 0.24335 0.0635 64.4 3.830 0.0003
## . faces exchange - top 0.05172 0.0635 64.4 0.814 0.4186
## . faces exchange - bottom 0.10490 0.0635 64.4 1.651 0.1036
## . faces top - bottom 0.05318 0.0635 64.4 0.837 0.4057
## . words intact - exchange -0.00969 0.0635 64.4 -0.153 0.8792
## . words intact - top 0.23277 0.0635 64.4 3.664 0.0005
## . words intact - bottom 0.07142 0.0635 64.4 1.124 0.2651
## . words exchange - top 0.24246 0.0635 64.4 3.816 0.0003
## . words exchange - bottom 0.08112 0.0635 64.4 1.277 0.2063
## . words top - bottom -0.16135 0.0635 64.4 -2.540 0.01353.2.1.1.1.2 2 * 2
2(face vs. word)$$2(intact vs. exchange) ANOVA## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.21643 0.1188 14.4 1.822 0.0893
## exchange . faces - words 0.06828 0.1188 14.4 0.575 0.5743
## . faces intact - exchange 0.13845 0.0626 22.0 2.210 0.0378
## . words intact - exchange -0.00969 0.0626 22.0 -0.155 0.8784## Layout FaceWord contrast estimate SE df t.ratio p.value
## top . faces - words 0.2590 0.1142 13.1 2.268 0.0409
## bottom . faces - words 0.0445 0.1142 13.1 0.390 0.7030
## . faces top - bottom 0.0532 0.0531 21.3 1.001 0.3281
## . words top - bottom -0.1613 0.0531 21.3 -3.037 0.00623.2.1.1.2 Right FFA1
3.2.1.1.2.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 16 0.38 15.77 ** .13 .001
## 2 Layout 2.47, 39.55 0.05 4.74 ** .02 .010
## 3 FaceWord:Layout 2.40, 38.44 0.05 2.63 + .008 .08
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words 0.422 0.106 16 3.972 0.0011
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.15160 0.0513 48 2.956 0.0240
## intact - top 0.16925 0.0513 48 3.301 0.0095
## intact - bottom 0.14972 0.0513 48 2.920 0.0264
## exchange - top 0.01765 0.0513 48 0.344 0.9858
## exchange - bottom -0.00188 0.0513 48 -0.037 1.0000
## top - bottom -0.01953 0.0513 48 -0.381 0.9810
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.592937 0.1228 27.6 4.829 <.0001
## exchange . faces - words 0.339928 0.1228 27.6 2.768 0.0100
## top . faces - words 0.377181 0.1228 27.6 3.072 0.0047
## bottom . faces - words 0.376699 0.1228 27.6 3.068 0.0048
## . faces intact - exchange 0.278103 0.0719 96.0 3.869 0.0002
## . faces intact - top 0.277131 0.0719 96.0 3.856 0.0002
## . faces intact - bottom 0.257837 0.0719 96.0 3.587 0.0005
## . faces exchange - top -0.000972 0.0719 96.0 -0.014 0.9892
## . faces exchange - bottom -0.020266 0.0719 96.0 -0.282 0.7786
## . faces top - bottom -0.019294 0.0719 96.0 -0.268 0.7889
## . words intact - exchange 0.025094 0.0719 96.0 0.349 0.7278
## . words intact - top 0.061375 0.0719 96.0 0.854 0.3953
## . words intact - bottom 0.041599 0.0719 96.0 0.579 0.5641
## . words exchange - top 0.036282 0.0719 96.0 0.505 0.6149
## . words exchange - bottom 0.016506 0.0719 96.0 0.230 0.8189
## . words top - bottom -0.019776 0.0719 96.0 -0.275 0.78383.2.1.1.2.2 2 * 2
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.5929 0.1086 26.1 5.462 <.0001
## exchange . faces - words 0.3399 0.1086 26.1 3.131 0.0043
## . faces intact - exchange 0.2781 0.0709 30.4 3.924 0.0005
## . words intact - exchange 0.0251 0.0709 30.4 0.354 0.7258## Layout FaceWord contrast estimate SE df t.ratio p.value
## top . faces - words 0.3772 0.1356 20.8 2.783 0.0112
## bottom . faces - words 0.3767 0.1356 20.8 2.779 0.0113
## . faces top - bottom -0.0193 0.0711 32.0 -0.272 0.7877
## . words top - bottom -0.0198 0.0711 32.0 -0.278 0.78263.2.1.2 Plot
The above figure shows the neural respones (beta values) in FFA1 for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: “*p<0.1;**p<0.05;***p<0.01”
3.2.2 Decoding
3.2.2.1 One-sample t-test
3.2.2.2 Plot
The above figure shows the decoding accuracy in FFA1 for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: “*p<0.1;**p<0.05;***p<0.01”
3.2.3 Similarity of top + bottom to intact vs. exchange
3.2.3.1 One-sample t-test
3.2.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in FFA1. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
3.3 Label:FFA2
3.3.1 Univariate analyses
3.3.1.1 rm-ANOVA
3.3.1.1.1 Left FFA2
3.3.1.1.1.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 11 0.07 2.65 .01 .13
## 2 Layout 2.39, 26.30 0.02 6.90 ** .02 .003
## 3 FaceWord:Layout 2.31, 25.37 0.04 0.21 .001 .84
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words 0.0908 0.0558 11 1.628 0.1318
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.1022 0.0404 33 2.531 0.0734
## intact - top 0.1830 0.0404 33 4.532 0.0004
## intact - bottom 0.0863 0.0404 33 2.138 0.1624
## exchange - top 0.0808 0.0404 33 2.001 0.2082
## exchange - bottom -0.0159 0.0404 33 -0.394 0.9790
## top - bottom -0.0967 0.0404 33 -2.394 0.0980
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.13378 0.0822 35.6 1.627 0.1126
## exchange . faces - words 0.07368 0.0822 35.6 0.896 0.3762
## top . faces - words 0.06034 0.0822 35.6 0.734 0.4678
## bottom . faces - words 0.09558 0.0822 35.6 1.162 0.2528
## . faces intact - exchange 0.13228 0.0637 63.5 2.075 0.0420
## . faces intact - top 0.21976 0.0637 63.5 3.448 0.0010
## . faces intact - bottom 0.10544 0.0637 63.5 1.654 0.1030
## . faces exchange - top 0.08748 0.0637 63.5 1.373 0.1747
## . faces exchange - bottom -0.02684 0.0637 63.5 -0.421 0.6751
## . faces top - bottom -0.11433 0.0637 63.5 -1.794 0.0776
## . words intact - exchange 0.07219 0.0637 63.5 1.133 0.2617
## . words intact - top 0.14633 0.0637 63.5 2.296 0.0250
## . words intact - bottom 0.06724 0.0637 63.5 1.055 0.2954
## . words exchange - top 0.07414 0.0637 63.5 1.163 0.2491
## . words exchange - bottom -0.00494 0.0637 63.5 -0.078 0.9384
## . words top - bottom -0.07909 0.0637 63.5 -1.241 0.21933.3.1.1.1.2 2 * 2
2(face vs. word)$$2(intact vs. exchange) ANOVA## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.1338 0.0834 17.5 1.604 0.1267
## exchange . faces - words 0.0737 0.0834 17.5 0.883 0.3891
## . faces intact - exchange 0.1323 0.0627 21.6 2.110 0.0466
## . words intact - exchange 0.0722 0.0627 21.6 1.152 0.2621## Layout FaceWord contrast estimate SE df t.ratio p.value
## top . faces - words 0.0603 0.0810 21.8 0.745 0.4643
## bottom . faces - words 0.0956 0.0810 21.8 1.180 0.2507
## . faces top - bottom -0.1143 0.0612 16.4 -1.867 0.0799
## . words top - bottom -0.0791 0.0612 16.4 -1.291 0.21453.3.1.1.2 Right FFA2
3.3.1.1.2.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 12 0.21 9.41 ** .10 .010
## 2 Layout 2.50, 30.03 0.03 9.05 *** .03 .0004
## 3 FaceWord:Layout 2.42, 29.01 0.03 3.43 * .02 .04
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words 0.275 0.0896 12 3.068 0.0098
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.18696 0.0411 36 4.553 0.0003
## intact - top 0.18061 0.0411 36 4.398 0.0005
## intact - bottom 0.14401 0.0411 36 3.507 0.0065
## exchange - top -0.00635 0.0411 36 -0.155 0.9987
## exchange - bottom -0.04295 0.0411 36 -1.046 0.7240
## top - bottom -0.03660 0.0411 36 -0.891 0.8094
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.44008 0.1050 21.6 4.189 0.0004
## exchange . faces - words 0.16375 0.1050 21.6 1.559 0.1335
## top . faces - words 0.25395 0.1050 21.6 2.417 0.0245
## bottom . faces - words 0.24186 0.1050 21.6 2.302 0.0313
## . faces intact - exchange 0.32512 0.0607 71.5 5.353 <.0001
## . faces intact - top 0.27367 0.0607 71.5 4.506 <.0001
## . faces intact - bottom 0.24312 0.0607 71.5 4.003 0.0002
## . faces exchange - top -0.05145 0.0607 71.5 -0.847 0.3998
## . faces exchange - bottom -0.08200 0.0607 71.5 -1.350 0.1812
## . faces top - bottom -0.03055 0.0607 71.5 -0.503 0.6165
## . words intact - exchange 0.04879 0.0607 71.5 0.803 0.4244
## . words intact - top 0.08754 0.0607 71.5 1.441 0.1539
## . words intact - bottom 0.04490 0.0607 71.5 0.739 0.4621
## . words exchange - top 0.03875 0.0607 71.5 0.638 0.5255
## . words exchange - bottom -0.00389 0.0607 71.5 -0.064 0.9491
## . words top - bottom -0.04264 0.0607 71.5 -0.702 0.48493.3.1.1.2.2 2 * 2
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words 0.4401 0.1052 15.5 4.184 0.0007
## exchange . faces - words 0.1638 0.1052 15.5 1.557 0.1397
## . faces intact - exchange 0.3251 0.0583 23.4 5.577 <.0001
## . words intact - exchange 0.0488 0.0583 23.4 0.837 0.4110## Layout FaceWord contrast estimate SE df t.ratio p.value
## top . faces - words 0.2539 0.1049 19.1 2.421 0.0256
## bottom . faces - words 0.2419 0.1049 19.1 2.306 0.0325
## . faces top - bottom -0.0306 0.0609 19.7 -0.501 0.6216
## . words top - bottom -0.0426 0.0609 19.7 -0.700 0.49223.3.1.2 Plot
The above figure shows the neural respones (beta values) in FFA2 for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: “*p<0.1;**p<0.05;***p<0.01”
3.3.2 Decoding
3.3.2.1 One-sample t-test
3.3.2.2 Plot
The above figure shows the decoding accuracy in FFA2 for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: “*p<0.1;**p<0.05;***p<0.01”
3.3.3 Similarity of top + bottom to intact vs. exchange
3.3.3.1 One-sample t-test
3.3.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in FFA2. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
3.4 Label: left Visual Word Form Area (VWFA)
3.4.1 Univariate analyses
3.4.1.1 rm-ANOVA
3.4.1.1.0.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 17 0.21 100.25 *** .25 <.0001
## 2 Layout 2.53, 43.04 0.03 4.04 * .005 .02
## 3 FaceWord:Layout 2.57, 43.65 0.03 5.40 ** .005 .005
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words -0.773 0.0772 17 -10.012 <.0001
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange -0.05205 0.0368 51 -1.413 0.4974
## intact - top 0.07544 0.0368 51 2.048 0.1844
## intact - bottom 0.00925 0.0368 51 0.251 0.9944
## exchange - top 0.12748 0.0368 51 3.460 0.0059
## exchange - bottom 0.06130 0.0368 51 1.664 0.3531
## top - bottom -0.06619 0.0368 51 -1.796 0.2868
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.69862 0.0881 27.9 -7.932 <.0001
## exchange . faces - words -0.90436 0.0881 27.9 -10.268 <.0001
## top . faces - words -0.65964 0.0881 27.9 -7.490 <.0001
## bottom . faces - words -0.82940 0.0881 27.9 -9.417 <.0001
## . faces intact - exchange 0.05082 0.0505 101.6 1.005 0.3171
## . faces intact - top 0.05595 0.0505 101.6 1.107 0.2710
## . faces intact - bottom 0.07464 0.0505 101.6 1.477 0.1429
## . faces exchange - top 0.00513 0.0505 101.6 0.101 0.9194
## . faces exchange - bottom 0.02382 0.0505 101.6 0.471 0.6385
## . faces top - bottom 0.01869 0.0505 101.6 0.370 0.7123
## . words intact - exchange -0.15491 0.0505 101.6 -3.065 0.0028
## . words intact - top 0.09493 0.0505 101.6 1.878 0.0633
## . words intact - bottom -0.05614 0.0505 101.6 -1.111 0.2694
## . words exchange - top 0.24984 0.0505 101.6 4.943 <.0001
## . words exchange - bottom 0.09878 0.0505 101.6 1.954 0.0534
## . words top - bottom -0.15106 0.0505 101.6 -2.989 0.00353.4.1.1.0.2 2 * 2
2(face vs. word)$$2(intact vs. exchange) ANOVA## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.6986 0.0872 21.1 -8.015 <.0001
## exchange . faces - words -0.9044 0.0872 21.1 -10.376 <.0001
## . faces intact - exchange 0.0508 0.0390 33.8 1.302 0.2017
## . words intact - exchange -0.1549 0.0390 33.8 -3.969 0.0004## Layout FaceWord contrast estimate SE df t.ratio p.value
## top . faces - words -0.6596 0.089 24.1 -7.413 <.0001
## bottom . faces - words -0.8294 0.089 24.1 -9.321 <.0001
## . faces top - bottom 0.0187 0.053 34.0 0.353 0.7263
## . words top - bottom -0.1511 0.053 34.0 -2.852 0.00733.4.1.2 Plot
The above figure shows the neural respones (beta values) in VWFA for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: *, p < .05
3.4.2 Decoding
3.4.2.1 One-sample t-test
3.4.2.2 Plot
The above figure shows the decoding accuracy in VWFA for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: ***, p <.001
3.4.3 Similarity of top + bottom to intact vs. exchange
3.4.3.1 One-sample t-test
3.4.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in VWFA. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
3.5 Label:Lateral Occipital Cortex
3.5.1 Univariate analyses
3.5.1.1 rm-ANOVA
3.5.1.1.1 Left LO
3.5.1.1.1.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 18 0.22 22.93 *** .06 .0001
## 2 Layout 2.45, 44.18 0.04 3.92 * .005 .02
## 3 FaceWord:Layout 2.42, 43.48 0.03 0.40 .0004 .71
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words -0.362 0.0755 18 -4.789 0.0001
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.00662 0.0425 54 0.156 0.9986
## intact - top 0.12934 0.0425 54 3.043 0.0184
## intact - bottom 0.04907 0.0425 54 1.155 0.6577
## exchange - top 0.12272 0.0425 54 2.888 0.0277
## exchange - bottom 0.04245 0.0425 54 0.999 0.7507
## top - bottom -0.08027 0.0425 54 -1.889 0.2449
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.32762 0.0872 30.9 -3.757 0.0007
## exchange . faces - words -0.34424 0.0872 30.9 -3.948 0.0004
## top . faces - words -0.37437 0.0872 30.9 -4.293 0.0002
## bottom . faces - words -0.39984 0.0872 30.9 -4.585 0.0001
## . faces intact - exchange 0.01493 0.0555 104.8 0.269 0.7883
## . faces intact - top 0.15271 0.0555 104.8 2.754 0.0070
## . faces intact - bottom 0.08517 0.0555 104.8 1.536 0.1276
## . faces exchange - top 0.13779 0.0555 104.8 2.484 0.0146
## . faces exchange - bottom 0.07025 0.0555 104.8 1.267 0.2081
## . faces top - bottom -0.06754 0.0555 104.8 -1.218 0.2260
## . words intact - exchange -0.00169 0.0555 104.8 -0.030 0.9757
## . words intact - top 0.10596 0.0555 104.8 1.911 0.0588
## . words intact - bottom 0.01296 0.0555 104.8 0.234 0.8157
## . words exchange - top 0.10765 0.0555 104.8 1.941 0.0549
## . words exchange - bottom 0.01465 0.0555 104.8 0.264 0.7922
## . words top - bottom -0.09300 0.0555 104.8 -1.677 0.09653.5.1.1.1.2 2 * 2
2(face vs. word)$$2(intact vs. exchange) ANOVA## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.32762 0.0955 22 -3.429 0.0024
## exchange . faces - words -0.34424 0.0955 22 -3.603 0.0016
## . faces intact - exchange 0.01493 0.0469 35 0.318 0.7520
## . words intact - exchange -0.00169 0.0469 35 -0.036 0.9714## Layout FaceWord contrast estimate SE df t.ratio p.value
## top . faces - words -0.3744 0.078 23.2 -4.801 0.0001
## bottom . faces - words -0.3998 0.078 23.2 -5.128 <.0001
## . faces top - bottom -0.0675 0.054 29.6 -1.251 0.2206
## . words top - bottom -0.0930 0.054 29.6 -1.723 0.09533.5.1.1.2 Right LO
3.5.1.1.2.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 17 0.20 8.43 ** .02 .010
## 2 Layout 2.18, 37.07 0.07 1.47 .002 .24
## 3 FaceWord:Layout 2.63, 44.74 0.03 1.27 .001 .30
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## faces - words -0.217 0.0747 17 -2.904 0.0099
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.0825 0.0532 51 1.551 0.4156
## intact - top 0.1037 0.0532 51 1.948 0.2212
## intact - bottom 0.0449 0.0532 51 0.843 0.8339
## exchange - top 0.0212 0.0532 51 0.398 0.9785
## exchange - bottom -0.0377 0.0532 51 -0.708 0.8935
## top - bottom -0.0589 0.0532 51 -1.106 0.6877
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.14282 0.0893 32.6 -1.600 0.1193
## exchange . faces - words -0.29775 0.0893 32.6 -3.335 0.0021
## top . faces - words -0.21835 0.0893 32.6 -2.446 0.0200
## bottom . faces - words -0.20910 0.0893 32.6 -2.342 0.0254
## . faces intact - exchange 0.16001 0.0665 94.5 2.406 0.0181
## . faces intact - top 0.14149 0.0665 94.5 2.127 0.0360
## . faces intact - bottom 0.07800 0.0665 94.5 1.173 0.2439
## . faces exchange - top -0.01853 0.0665 94.5 -0.279 0.7812
## . faces exchange - bottom -0.08202 0.0665 94.5 -1.233 0.2206
## . faces top - bottom -0.06349 0.0665 94.5 -0.954 0.3423
## . words intact - exchange 0.00508 0.0665 94.5 0.076 0.9393
## . words intact - top 0.06595 0.0665 94.5 0.992 0.3240
## . words intact - bottom 0.01171 0.0665 94.5 0.176 0.8606
## . words exchange - top 0.06087 0.0665 94.5 0.915 0.3625
## . words exchange - bottom 0.00663 0.0665 94.5 0.100 0.9208
## . words top - bottom -0.05424 0.0665 94.5 -0.815 0.41693.5.1.1.2.2 2 * 2
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . faces - words -0.14282 0.0929 22.8 -1.537 0.1380
## exchange . faces - words -0.29775 0.0929 22.8 -3.205 0.0040
## . faces intact - exchange 0.16001 0.0573 32.6 2.794 0.0087
## . words intact - exchange 0.00508 0.0573 32.6 0.089 0.9299## Layout FaceWord contrast estimate SE df t.ratio p.value
## top . faces - words -0.2184 0.0855 26.4 -2.554 0.0167
## bottom . faces - words -0.2091 0.0855 26.4 -2.446 0.0214
## . faces top - bottom -0.0635 0.0614 33.7 -1.033 0.3088
## . words top - bottom -0.0542 0.0614 33.7 -0.883 0.38363.5.1.2 Plot
The above figure shows the neural respones (beta values) in LO for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: *, p < .05
3.5.2 Decoding
3.5.2.1 One-sample t-test
3.5.2.2 Plot
The above figure shows the decoding accuracy in LO for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: , p < .01; *, p <.001
3.5.3 Similarity of top + bottom to intact vs. exchange
3.5.3.1 One-sample t-test
3.5.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in LO. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
4 Experiment 2: English and Chinese characters for Caucasian participants
4.1 Load and clean data
4.1.1 Label (ROI) information
4.1.1.1 Size of labels
The above table displays the size (in mm2) of each label for each participant. (NA denotes that this label is not available for that particiapnt.)
4.1.1.2 Number of vertices for each label
The above table displays the number of vertices for each label and each participant. (NA denotes that this label is not available for that particiapnt.)
4.1.1.3 Number of participants for each ROI
4.1.1.4 Number of remaining participants
The above table dispalys the number of participants included in the following analyses for each ROI. (VWFA is only found on the left hemisphere.)
4.1.2 Data for univariate analyses
4.1.3 Data of decoding
4.1.4 Data for the Similarity of top + bottom
4.2 Label:FFA1
4.2.1 Univariate analyses
4.2.1.1 rm-ANOVA
4.2.1.1.1 Left FFA1
4.2.1.1.1.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 11 0.22 12.53 ** .10 .005
## 2 Layout 1.73, 19.00 0.03 3.34 + .007 .06
## 3 FaceWord:Layout 2.25, 24.78 0.04 4.10 * .01 .03
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## English - Chinese 0.338 0.0954 11 3.539 0.0046
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange -0.10656 0.0394 33 -2.706 0.0499
## intact - partA 0.00125 0.0394 33 0.032 1.0000
## intact - partB -0.04698 0.0394 33 -1.193 0.6354
## exchange - partA 0.10781 0.0394 33 2.738 0.0464
## exchange - partB 0.05958 0.0394 33 1.513 0.4414
## partA - partB -0.04823 0.0394 33 -1.225 0.6159
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.34336 0.1134 20.8 3.027 0.0065
## exchange . English - Chinese 0.44662 0.1134 20.8 3.937 0.0008
## partA . English - Chinese 0.13346 0.1134 20.8 1.176 0.2527
## partB . English - Chinese 0.42759 0.1134 20.8 3.769 0.0011
## . English intact - exchange -0.15819 0.0637 62.5 -2.483 0.0157
## . English intact - partA 0.10620 0.0637 62.5 1.667 0.1005
## . English intact - partB -0.08909 0.0637 62.5 -1.398 0.1669
## . English exchange - partA 0.26440 0.0637 62.5 4.150 0.0001
## . English exchange - partB 0.06910 0.0637 62.5 1.085 0.2823
## . English partA - partB -0.19530 0.0637 62.5 -3.065 0.0032
## . Chinese intact - exchange -0.05493 0.0637 62.5 -0.862 0.3919
## . Chinese intact - partA -0.10370 0.0637 62.5 -1.628 0.1086
## . Chinese intact - partB -0.00486 0.0637 62.5 -0.076 0.9394
## . Chinese exchange - partA -0.04877 0.0637 62.5 -0.765 0.4469
## . Chinese exchange - partB 0.05007 0.0637 62.5 0.786 0.4349
## . Chinese partA - partB 0.09884 0.0637 62.5 1.551 0.12594.2.1.1.1.2 2 * 2
2(face vs. word)$$2(intact vs. exchange) ANOVA## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.3434 0.1178 16.8 2.915 0.0098
## exchange . English - Chinese 0.4466 0.1178 16.8 3.791 0.0015
## . English intact - exchange -0.1582 0.0741 21.7 -2.134 0.0444
## . Chinese intact - exchange -0.0549 0.0741 21.7 -0.741 0.4666## Layout FaceWord contrast estimate SE df t.ratio p.value
## partA . English - Chinese 0.1335 0.1089 18.6 1.225 0.2358
## partB . English - Chinese 0.4276 0.1089 18.6 3.926 0.0009
## . English partA - partB -0.1953 0.0647 16.0 -3.019 0.0082
## . Chinese partA - partB 0.0988 0.0647 16.0 1.528 0.14614.2.1.1.2 Right FFA1
4.2.1.1.2.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 14 0.13 0.65 .008 .43
## 2 Layout 2.74, 38.34 0.03 2.08 .02 .12
## 3 FaceWord:Layout 2.18, 30.55 0.03 1.39 .009 .26
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## English - Chinese -0.0522 0.0648 14 -0.805 0.4341
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.0605 0.0406 42 1.490 0.4522
## intact - partA -0.0299 0.0406 42 -0.738 0.8814
## intact - partB 0.0445 0.0406 42 1.098 0.6929
## exchange - partA -0.0904 0.0406 42 -2.228 0.1323
## exchange - partB -0.0159 0.0406 42 -0.393 0.9792
## partA - partB 0.0745 0.0406 42 1.835 0.2716
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.00632 0.0794 29.1 0.080 0.9371
## exchange . English - Chinese -0.13621 0.0794 29.1 -1.716 0.0969
## partA . English - Chinese -0.06054 0.0794 29.1 -0.763 0.4519
## partB . English - Chinese -0.01847 0.0794 29.1 -0.233 0.8177
## . English intact - exchange 0.13175 0.0552 83.5 2.387 0.0192
## . English intact - partA 0.00350 0.0552 83.5 0.063 0.9496
## . English intact - partB 0.05694 0.0552 83.5 1.032 0.3052
## . English exchange - partA -0.12825 0.0552 83.5 -2.324 0.0226
## . English exchange - partB -0.07481 0.0552 83.5 -1.355 0.1789
## . English partA - partB 0.05344 0.0552 83.5 0.968 0.3357
## . Chinese intact - exchange -0.01078 0.0552 83.5 -0.195 0.8456
## . Chinese intact - partA -0.06337 0.0552 83.5 -1.148 0.2542
## . Chinese intact - partB 0.03215 0.0552 83.5 0.582 0.5618
## . Chinese exchange - partA -0.05258 0.0552 83.5 -0.953 0.3434
## . Chinese exchange - partB 0.04293 0.0552 83.5 0.778 0.4389
## . Chinese partA - partB 0.09551 0.0552 83.5 1.731 0.08724.2.1.1.2.2 2 * 2
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.00632 0.0835 17.6 0.076 0.9405
## exchange . English - Chinese -0.13621 0.0835 17.6 -1.631 0.1206
## . English intact - exchange 0.13175 0.0535 23.4 2.463 0.0215
## . Chinese intact - exchange -0.01078 0.0535 23.4 -0.202 0.8420## Layout FaceWord contrast estimate SE df t.ratio p.value
## partA . English - Chinese -0.0605 0.0751 23.5 -0.806 0.4280
## partB . English - Chinese -0.0185 0.0751 23.5 -0.246 0.8078
## . English partA - partB 0.0534 0.0589 27.8 0.907 0.3723
## . Chinese partA - partB 0.0955 0.0589 27.8 1.621 0.11644.2.1.2 Plot
The above figure shows the neural respones (beta values) in FFA1 for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: “*p<0.1;**p<0.05;***p<0.01”
4.2.2 Decoding
4.2.2.1 One-sample t-test
4.2.2.2 Plot
The above figure shows the decoding accuracy in FFA1 for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: “*p<0.1;**p<0.05;***p<0.01”
4.2.3 Similarity of top + bottom to intact vs. exchange
4.2.3.1 One-sample t-test
4.2.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in FFA1. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
4.3 Label:FFA2
4.3.1 Univariate analyses
4.3.1.1 rm-ANOVA
4.3.1.1.1 Left FFA2
4.3.1.1.1.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 12 0.18 8.65 * .08 .01
## 2 Layout 2.52, 30.24 0.02 0.84 .002 .47
## 3 FaceWord:Layout 2.56, 30.70 0.03 2.83 + .01 .06
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## English - Chinese 0.247 0.084 12 2.940 0.0124
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.01909 0.0343 36 0.556 0.9442
## intact - partA 0.02635 0.0343 36 0.768 0.8683
## intact - partB -0.02327 0.0343 36 -0.678 0.9047
## exchange - partA 0.00726 0.0343 36 0.212 0.9966
## exchange - partB -0.04236 0.0343 36 -1.234 0.6095
## partA - partB -0.04962 0.0343 36 -1.446 0.4799
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.3426 0.1002 23.0 3.418 0.0024
## exchange . English - Chinese 0.2466 0.1002 23.0 2.460 0.0218
## partA . English - Chinese 0.0986 0.1002 23.0 0.984 0.3355
## partB . English - Chinese 0.2999 0.1002 23.0 2.992 0.0065
## . English intact - exchange 0.0671 0.0564 67.5 1.191 0.2379
## . English intact - partA 0.1484 0.0564 67.5 2.633 0.0105
## . English intact - partB -0.0019 0.0564 67.5 -0.034 0.9732
## . English exchange - partA 0.0813 0.0564 67.5 1.442 0.1539
## . English exchange - partB -0.0690 0.0564 67.5 -1.224 0.2250
## . English partA - partB -0.1503 0.0564 67.5 -2.666 0.0096
## . Chinese intact - exchange -0.0289 0.0564 67.5 -0.513 0.6096
## . Chinese intact - partA -0.0957 0.0564 67.5 -1.697 0.0942
## . Chinese intact - partB -0.0446 0.0564 67.5 -0.792 0.4311
## . Chinese exchange - partA -0.0667 0.0564 67.5 -1.184 0.2404
## . Chinese exchange - partB -0.0157 0.0564 67.5 -0.279 0.7811
## . Chinese partA - partB 0.0510 0.0564 67.5 0.905 0.36854.3.1.1.1.2 2 * 2
2(face vs. word)$$2(intact vs. exchange) ANOVA## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.3426 0.1014 19.6 3.379 0.0030
## exchange . English - Chinese 0.2466 0.1014 19.6 2.432 0.0247
## . English intact - exchange 0.0671 0.0607 19.7 1.105 0.2825
## . Chinese intact - exchange -0.0289 0.0607 19.7 -0.476 0.6392## Layout FaceWord contrast estimate SE df t.ratio p.value
## partA . English - Chinese 0.0986 0.0991 19.0 0.995 0.3321
## partB . English - Chinese 0.2999 0.0991 19.0 3.027 0.0069
## . English partA - partB -0.1503 0.0643 23.4 -2.336 0.0284
## . Chinese partA - partB 0.0510 0.0643 23.4 0.793 0.43574.3.1.1.2 Right FFA2
4.3.1.1.2.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 17 0.05 0.00 <.0001 .99
## 2 Layout 2.55, 43.43 0.01 0.23 .0009 .85
## 3 FaceWord:Layout 2.28, 38.70 0.02 0.72 .005 .51
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## English - Chinese 0.000317 0.0355 17 0.009 0.9930
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.01582 0.0232 51 0.682 0.9034
## intact - partA -0.00143 0.0232 51 -0.062 0.9999
## intact - partB 0.00305 0.0232 51 0.131 0.9992
## exchange - partA -0.01725 0.0232 51 -0.744 0.8789
## exchange - partB -0.01277 0.0232 51 -0.551 0.9459
## partA - partB 0.00447 0.0232 51 0.193 0.9974
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.04175 0.0529 56.1 0.789 0.4335
## exchange . English - Chinese -0.00244 0.0529 56.1 -0.046 0.9634
## partA . English - Chinese -0.05021 0.0529 56.1 -0.949 0.3468
## partB . English - Chinese 0.01216 0.0529 56.1 0.230 0.8190
## . English intact - exchange 0.03791 0.0396 92.9 0.958 0.3403
## . English intact - partA 0.04455 0.0396 92.9 1.126 0.2630
## . English intact - partB 0.01784 0.0396 92.9 0.451 0.6531
## . English exchange - partA 0.00664 0.0396 92.9 0.168 0.8671
## . English exchange - partB -0.02007 0.0396 92.9 -0.507 0.6130
## . English partA - partB -0.02671 0.0396 92.9 -0.675 0.5012
## . Chinese intact - exchange -0.00628 0.0396 92.9 -0.159 0.8743
## . Chinese intact - partA -0.04741 0.0396 92.9 -1.198 0.2338
## . Chinese intact - partB -0.01175 0.0396 92.9 -0.297 0.7672
## . Chinese exchange - partA -0.04113 0.0396 92.9 -1.040 0.3011
## . Chinese exchange - partB -0.00547 0.0396 92.9 -0.138 0.8903
## . Chinese partA - partB 0.03566 0.0396 92.9 0.901 0.36974.3.1.1.2.2 2 * 2
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.04175 0.0560 28.9 0.746 0.4619
## exchange . English - Chinese -0.00244 0.0560 28.9 -0.044 0.9656
## . English intact - exchange 0.03791 0.0409 33.8 0.927 0.3607
## . Chinese intact - exchange -0.00628 0.0409 33.8 -0.153 0.8790## Layout FaceWord contrast estimate SE df t.ratio p.value
## partA . English - Chinese -0.0502 0.0497 33.5 -1.011 0.3192
## partB . English - Chinese 0.0122 0.0497 33.5 0.245 0.8080
## . English partA - partB -0.0267 0.0420 25.9 -0.635 0.5308
## . Chinese partA - partB 0.0357 0.0420 25.9 0.848 0.40424.3.1.2 Plot
The above figure shows the neural respones (beta values) in FFA2 for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: “*p<0.1;**p<0.05;***p<0.01”
4.3.2 Decoding
4.3.2.1 One-sample t-test
4.3.2.2 Plot
The above figure shows the decoding accuracy in FFA2 for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: “*p<0.1;**p<0.05;***p<0.01”
4.3.3 Similarity of top + bottom to intact vs. exchange
4.3.3.1 One-sample t-test
4.3.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in FFA2. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
4.4 Label: left Visual Word Form Area (VWFA)
4.4.1 Univariate analyses
4.4.1.1 rm-ANOVA
4.4.1.1.0.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 13 0.26 66.19 *** .35 <.0001
## 2 Layout 2.25, 29.19 0.03 10.51 *** .02 .0002
## 3 FaceWord:Layout 1.62, 21.06 0.06 9.23 ** .03 .002
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## English - Chinese 0.786 0.0966 13 8.135 <.0001
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange -0.1739 0.0366 39 -4.748 0.0002
## intact - partA -0.0150 0.0366 39 -0.411 0.9763
## intact - partB -0.1217 0.0366 39 -3.323 0.0101
## exchange - partA 0.1588 0.0366 39 4.337 0.0006
## exchange - partB 0.0522 0.0366 39 1.425 0.4917
## partA - partB -0.1066 0.0366 39 -2.912 0.0289
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.7486 0.1124 22.9 6.662 <.0001
## exchange . English - Chinese 0.9659 0.1124 22.9 8.596 <.0001
## partA . English - Chinese 0.5173 0.1124 22.9 4.604 0.0001
## partB . English - Chinese 0.9114 0.1124 22.9 8.111 <.0001
## . English intact - exchange -0.2825 0.0595 73.7 -4.749 <.0001
## . English intact - partA 0.1006 0.0595 73.7 1.692 0.0950
## . English intact - partB -0.2031 0.0595 73.7 -3.414 0.0010
## . English exchange - partA 0.3831 0.0595 73.7 6.441 <.0001
## . English exchange - partB 0.0794 0.0595 73.7 1.335 0.1859
## . English partA - partB -0.3037 0.0595 73.7 -5.106 <.0001
## . Chinese intact - exchange -0.0652 0.0595 73.7 -1.096 0.2766
## . Chinese intact - partA -0.1307 0.0595 73.7 -2.197 0.0312
## . Chinese intact - partB -0.0403 0.0595 73.7 -0.677 0.5006
## . Chinese exchange - partA -0.0655 0.0595 73.7 -1.101 0.2746
## . Chinese exchange - partB 0.0249 0.0595 73.7 0.419 0.6762
## . Chinese partA - partB 0.0904 0.0595 73.7 1.520 0.13284.4.1.1.0.2 2 * 2
2(face vs. word)$$2(intact vs. exchange) ANOVA## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese 0.7486 0.1161 18.2 6.448 <.0001
## exchange . English - Chinese 0.9659 0.1161 18.2 8.319 <.0001
## . English intact - exchange -0.2825 0.0625 25.1 -4.521 0.0001
## . Chinese intact - exchange -0.0652 0.0625 25.1 -1.043 0.3067## Layout FaceWord contrast estimate SE df t.ratio p.value
## partA . English - Chinese 0.5173 0.1085 22.3 4.768 0.0001
## partB . English - Chinese 0.9114 0.1085 22.3 8.401 <.0001
## . English partA - partB -0.3037 0.0693 21.6 -4.383 0.0002
## . Chinese partA - partB 0.0904 0.0693 21.6 1.305 0.20574.4.1.2 Plot
The above figure shows the neural respones (beta values) in VWFA for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: *, p < .05
4.4.2 Decoding
4.4.2.1 One-sample t-test
4.4.2.2 Plot
The above figure shows the decoding accuracy in VWFA for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: ***, p <.001
4.4.3 Similarity of top + bottom to intact vs. exchange
4.4.3.1 One-sample t-test
4.4.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in VWFA. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
4.5 Label:Lateral Occipital Cortex
4.5.1 Univariate analyses
4.5.1.1 rm-ANOVA
4.5.1.1.1 Left LO
4.5.1.1.1.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 15 0.19 4.52 + .01 .05
## 2 Layout 1.75, 26.31 0.05 1.33 .002 .28
## 3 FaceWord:Layout 2.06, 30.90 0.07 1.75 .004 .19
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## English - Chinese -0.163 0.0768 15 -2.126 0.0506
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange -0.05319 0.0414 45 -1.284 0.5779
## intact - partA -0.08137 0.0414 45 -1.964 0.2170
## intact - partB -0.04894 0.0414 45 -1.181 0.6419
## exchange - partA -0.02818 0.0414 45 -0.680 0.9042
## exchange - partB 0.00426 0.0414 45 0.103 0.9996
## partA - partB 0.03244 0.0414 45 0.783 0.8619
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese -0.1079 0.1006 37.8 -1.073 0.2903
## exchange . English - Chinese -0.0773 0.1006 37.8 -0.768 0.4470
## partA . English - Chinese -0.3014 0.1006 37.8 -2.996 0.0048
## partB . English - Chinese -0.1661 0.1006 37.8 -1.651 0.1070
## . English intact - exchange -0.0685 0.0673 85.0 -1.017 0.3120
## . English intact - partA 0.0154 0.0673 85.0 0.228 0.8201
## . English intact - partB -0.0198 0.0673 85.0 -0.295 0.7691
## . English exchange - partA 0.0838 0.0673 85.0 1.245 0.2164
## . English exchange - partB 0.0487 0.0673 85.0 0.723 0.4719
## . English partA - partB -0.0352 0.0673 85.0 -0.523 0.6025
## . Chinese intact - exchange -0.0379 0.0673 85.0 -0.563 0.5750
## . Chinese intact - partA -0.1781 0.0673 85.0 -2.645 0.0097
## . Chinese intact - partB -0.0780 0.0673 85.0 -1.159 0.2497
## . Chinese exchange - partA -0.1402 0.0673 85.0 -2.082 0.0403
## . Chinese exchange - partB -0.0401 0.0673 85.0 -0.596 0.5527
## . Chinese partA - partB 0.1001 0.0673 85.0 1.486 0.14094.5.1.1.1.2 2 * 2
2(face vs. word)$$2(intact vs. exchange) ANOVA## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese -0.1079 0.0962 21.3 -1.122 0.2745
## exchange . English - Chinese -0.0773 0.0962 21.3 -0.804 0.4305
## . English intact - exchange -0.0685 0.0727 26.4 -0.942 0.3547
## . Chinese intact - exchange -0.0379 0.0727 26.4 -0.521 0.6065## Layout FaceWord contrast estimate SE df t.ratio p.value
## partA . English - Chinese -0.3014 0.105 29.6 -2.875 0.0074
## partB . English - Chinese -0.1661 0.105 29.6 -1.585 0.1236
## . English partA - partB -0.0352 0.075 19.5 -0.470 0.6439
## . Chinese partA - partB 0.1001 0.075 19.5 1.335 0.19734.5.1.1.2 Right LO
4.5.1.1.2.1 4 * 2
## Anova Table (Type 3 tests)
##
## Response: Response
## Effect df MSE F ges p.value
## 1 FaceWord 1, 16 0.15 44.67 *** .03 <.0001
## 2 Layout 2.30, 36.79 0.07 3.03 + .002 .05
## 3 FaceWord:Layout 2.18, 34.93 0.04 1.05 .0005 .36
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
Posthoc analysis for the main effects:
## contrast estimate SE df t.ratio p.value
## English - Chinese -0.438 0.0655 16 -6.684 <.0001
##
## Results are averaged over the levels of: Layout## contrast estimate SE df t.ratio p.value
## intact - exchange 0.08614 0.0568 48 1.516 0.4360
## intact - partA -0.08487 0.0568 48 -1.494 0.4491
## intact - partB -0.00697 0.0568 48 -0.123 0.9993
## exchange - partA -0.17101 0.0568 48 -3.010 0.0209
## exchange - partB -0.09311 0.0568 48 -1.639 0.3671
## partA - partB 0.07790 0.0568 48 1.371 0.5233
##
## Results are averaged over the levels of: FaceWord
## P value adjustment: tukey method for comparing a family of 4 estimates
Results of simple effect analysis (uncorrected):
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese -0.3835 0.0837 37.6 -4.584 <.0001
## exchange . English - Chinese -0.5192 0.0837 37.6 -6.206 <.0001
## partA . English - Chinese -0.3966 0.0837 37.6 -4.741 <.0001
## partB . English - Chinese -0.4507 0.0837 37.6 -5.388 <.0001
## . English intact - exchange 0.1540 0.0710 88.9 2.170 0.0327
## . English intact - partA -0.0783 0.0710 88.9 -1.103 0.2729
## . English intact - partB 0.0267 0.0710 88.9 0.376 0.7081
## . English exchange - partA -0.2323 0.0710 88.9 -3.273 0.0015
## . English exchange - partB -0.1273 0.0710 88.9 -1.794 0.0762
## . English partA - partB 0.1050 0.0710 88.9 1.479 0.1427
## . Chinese intact - exchange 0.0183 0.0710 88.9 0.257 0.7975
## . Chinese intact - partA -0.0914 0.0710 88.9 -1.288 0.2010
## . Chinese intact - partB -0.0406 0.0710 88.9 -0.572 0.5687
## . Chinese exchange - partA -0.1097 0.0710 88.9 -1.546 0.1257
## . Chinese exchange - partB -0.0589 0.0710 88.9 -0.830 0.4090
## . Chinese partA - partB 0.0508 0.0710 88.9 0.716 0.47574.5.1.1.2.2 2 * 2
## Layout FaceWord contrast estimate SE df t.ratio p.value
## intact . English - Chinese -0.3835 0.0832 25.8 -4.607 0.0001
## exchange . English - Chinese -0.5192 0.0832 25.8 -6.238 <.0001
## . English intact - exchange 0.1540 0.0712 29.3 2.164 0.0388
## . Chinese intact - exchange 0.0183 0.0712 29.3 0.257 0.7993## Layout FaceWord contrast estimate SE df t.ratio p.value
## partA . English - Chinese -0.3966 0.0841 29.6 -4.717 0.0001
## partB . English - Chinese -0.4507 0.0841 29.6 -5.361 <.0001
## . English partA - partB 0.1050 0.0629 29.6 1.669 0.1056
## . Chinese partA - partB 0.0508 0.0629 29.6 0.808 0.42544.5.1.2 Plot
The above figure shows the neural respones (beta values) in LO for each condition. The numbers are the p-values for the tests of differences between intact vs. exchange in that condition. Error bars represent 95% confidence intervals. Note: *, p < .05
4.5.2 Decoding
4.5.2.1 One-sample t-test
4.5.2.2 Plot
The above figure shows the decoding accuracy in LO for each pair. The numbers are the p-values for the one-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals. Note: , p < .01; *, p <.001
4.5.3 Similarity of top + bottom to intact vs. exchange
4.5.3.1 One-sample t-test
4.5.3.2 Plot
The above figure shows the probability of top+bottom being decoded as exchange conditions in LO. Patterns of top and bottom were combined with different weights, i.e., “face_top0.25-face_bottom0.75” denotes the linear combinations of face_top and face_bottom with the weights of 0.25/0.75. The numbers are the p-values for the two-tail one-sample t-tests against the chance level (0.5) in that condition. Error bars represent 95% confidence intervals.
5 Decoding in LO with different area sizes
Labels for LO were defined with the maximum area of 100, 150, 200 and 300 mm^2, respecitvely.
5.1 Experiment 1
5.1.1 LO Label (ROI) information
5.1.1.1 Size of labels
The above table displays the size (in mm2) of each label for each participant. (NA denotes that this label is not available for that particiapnt.)
5.1.1.2 Number of vertices for each label
The above table displays the number of vertices for each label and each participant. (NA denotes that this label is not available for that particiapnt.)
5.1.1.3 Number of participants for each ROI
5.1.1.4 Number of remaining participants
The above table dispalys the number of participants included in the following analyses for each ROI. (VWFA is only found on the left hemisphere.)
5.1.2 Decoding
5.1.2.1 One-sample t-test
5.2 Experiment 2
5.2.1 LO Label (ROI) information
5.2.1.1 Size of labels
The above table displays the size (in mm2) of each label for each participant. (NA denotes that this label is not available for that particiapnt.)
5.2.1.2 Number of vertices for each label
The above table displays the number of vertices for each label and each participant. (NA denotes that this label is not available for that particiapnt.)
5.2.1.3 Number of participants for each ROI
5.2.1.4 Number of remaining participants
The above table dispalys the number of participants included in the following analyses for each ROI. (VWFA is only found on the left hemisphere.)
5.2.2 Decoding
5.2.2.1 One-sample t-test
5.3 Plot
5.3.0.0.1 Intact faces vs. words
5.3.1 Intact vs. exchange
5.3.2 Top vs. bottom
6 Plots
6.1 Decoding
6.1.1 All intact stimuli
6.1.2 Intact vs. exchange
6.1.3 Top vs. bottom
6.2 Univariate analysis
6.2.1 Intact vs. exchange
6.2.2 Top vs. bottom
7 Versions of packages used
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] tools stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggpubr_0.2.5 magrittr_2.0.1 emmeans_1.4.7 lmerTest_3.1-0 afex_0.25-1 lme4_1.1-21 Matrix_1.2-18 forcats_0.4.0 stringr_1.4.0 dplyr_0.8.5 purrr_0.3.3 readr_1.3.1 tidyr_1.0.2 tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] httr_1.4.1 jsonlite_1.7.1 splines_3.6.3 carData_3.0-3 modelr_0.1.5 assertthat_0.2.1 cellranger_1.1.0 yaml_2.2.1 numDeriv_2016.8-1.1 pillar_1.4.4 backports_1.1.5 lattice_0.20-38 glue_1.4.2 digest_0.6.27 ggsignif_0.6.0 rvest_0.3.5 minqa_1.2.4 colorspace_1.4-1 cowplot_1.0.0 htmltools_0.5.0 plyr_1.8.6 pkgconfig_2.0.3
## [23] broom_0.5.3.9000 haven_2.2.0 xtable_1.8-4 mvtnorm_1.0-11 scales_1.0.0 openxlsx_4.1.3 rio_0.5.16 generics_0.0.2 car_3.0-5 ellipsis_0.3.1 withr_2.1.2 cli_2.0.2 crayon_1.3.4 readxl_1.3.1 estimability_1.3 evaluate_0.14 fansi_0.4.1 nlme_3.1-144 MASS_7.3-51.5 xml2_1.2.2 foreign_0.8-75 data.table_1.12.6
## [45] hms_0.5.3 lifecycle_0.2.0 munsell_0.5.0 zip_2.0.4 compiler_3.6.3 rlang_0.4.8 grid_3.6.3 nloptr_1.2.1 rstudioapi_0.11 labeling_0.3 rmarkdown_2.1 boot_1.3-24 gtable_0.3.0 abind_1.4-5 curl_4.3 reshape2_1.4.3 R6_2.4.1 lubridate_1.7.4 knitr_1.30 stringi_1.5.3 parallel_3.6.3 Rcpp_1.0.4.6
## [67] vctrs_0.3.1 tidyselect_1.0.0 xfun_0.19 coda_0.19-3A work by Haiyang Jin